Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.

中文翻译：

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在具有多模态数据表示的介绍性数据科学课程中教授视觉可访问性

尽管有多种方式来表示数据模式和模型，但可视化主要是在许多数据科学课程中教授的，因为它的效率很高。这种依赖于视力的输出可能会对盲人和视力障碍者以及有学习障碍的人造成严重障碍。我们认为，教师需要教授多种数据表示方法，以便所有学生都能制作出更易于访问的数据产品。在本文中，我们认为可访问性应该作为数据科学课程的一部分早在入门课程中教授，这样无论学习者是否主修数据科学，他们都可以对可访问性有基本的了解。作为在两个不同机构教授可访问性作为我们低年级课程的一部分的数据科学教育工作者，